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The fireworks algorithm is an optimization algorithm for simulating the explosion phenomenon of fireworks. Because of its fast convergence and high precision, it is widely used in pattern recognition, optimal scheduling, and other fields. However, most of the existing research work on the fireworks algorithm is improved based on its defects, and little consideration is given to reducing the number of parameters of the fireworks algorithm. The original fireworks algorithm has too many parameters, which increases the cost of algorithm adjustment and is not conducive to engineering applications. In addition, in the fireworks population, the unselected individuals are discarded, thus causing a waste of their location information. To reduce the number of parameters of the original Fireworks Algorithm and make full use of the location information of discarded individuals, we propose a simplified version of the Fireworks Algorithm. It reduces the number of algorithm parameters by redesigning the explosion operator of the fireworks algorithm and constructs an adaptive explosion radius by using the historical optimal information to balance the local mining and global exploration capabilitie

This study introduces Dance of Fireworks, an interactive system designed to combat sedentary health risks by enhancing engagement in radio calisthenics. Leveraging mobile device cameras and lightweight pose estimation (PoseNet/TensorFlow Lite), the system extracts body keypoints, computes joint angles, and compares them with standardized motions to deliver real-time corrective feedback. To incentivize participation, it dynamically maps users' movements (such as joint angles and velocity) to customizable fireworks animations, rewarding improved accuracy with richer visual effects. Experiments involving 136 participants demonstrated a significant reduction in average joint angle errors from 21.3 degrees to 9.8 degrees (p < 0.01) over four sessions, with 93.4 percent of users affirming its exercise-promoting efficacy and 85.4 percent praising its entertainment value. The system operates without predefined motion templates or specialised hardware, enabling seamless integration into office environments. Future enhancements will focus on improving pose recognition accuracy, reducing latency, and adding features such as multiplayer interaction and music synchronisation. This work prese

We show that the support of the Grothendieck polynomial $\mathfrak G_w$ of any fireworks permutation is as large as possible: a monomial appears in $\mathfrak G_w$ if and only if it divides $\mathbf x^{\mathrm{wt}(\overline{D(w)})}$ and is divisible by some monomial appearing in the Schubert polynomial $\mathfrak S_w$. Our formula implies that the homogenization of $\mathfrak G_w$ has M-convex support. We also show that for any fireworks permutation $w\in S_n$, there exists a layered permutation $π(w)\in S_n$ so that $\mathrm{supp}(\mathfrak G_{π(w)})\supseteq \mathrm{supp}(\mathfrak G_w)$.

Many real-world problems can be transformed into optimization problems, which can be classified into convex and non-convex. Although convex problems are almost completely studied in theory, many related algorithms to many non-convex problems do not work well and we need more optimization techniques. As a swarm intelligence optimization algorithm, the Fireworks Algorithm(FWA) has been widely studied and applied to many real-world scenarios, even including large language model fine-tuning. But the current fireworks algorithm still has a number of problems. Firstly, as a heuristic algorithm, its performance on convex problems cannot match the SOTA results, and can even be said to be unsatisfactory; secondly, the sampling methods (explosion) of most FWA variants are still uniform sampling, which is actually inefficient in high dimensional cases. This work of ours proposes a new student's t-distribution based FWA(TFWA) with a solid theoretical foundation, which fully utilizes the advantage that student's t-distribution can adjust the parameters (degrees of freedom) and thus adjust the exploitation capability. We have fully experimented on mainstream benchmarks CEC2013 and CEC2017, which

Deformable image registration remains a fundamental task in clinical practice, yet solving registration problems involving complex deformations remains challenging. Current deep learning-based registration methods employ continuous deformation to model large deformations, which often suffer from accumulated registration errors and interpolation inaccuracies. Moreover, achieving satisfactory results with these frameworks typically requires a large number of cascade stages, demanding substantial computational resources. Therefore, we propose a novel approach, the field refinement framework (FiRework), tailored for unsupervised deformable registration, aiming to address these challenges. In FiRework, we redesign the continuous deformation framework to mitigate the aforementioned errors. Notably, our FiRework requires only one level of recursion during training and supports continuous inference, offering improved efficacy compared to continuous deformation frameworks. We conducted experiments on two brain MRI datasets, enhancing two existing deformable registration networks with FiRework. The experimental results demonstrate the superior performance of our proposed framework in deforma

Pa 30 -- the likely remnant of the Galactic type Iax supernova of 1181 AD -- displays an unusual, firework-like morphology, consisting of radial filaments extending from a common center, where a white dwarf (WD) currently drives a very fast wind (speed $\gtrsim 10^{4}$ km s$^{-1}$). We propose the filaments arose from the Rayleigh-Taylor-unstable nature of the interface between the circumstellar medium (CSM) and the shocked wind launched by the natal WD; the filaments then elongated intact due to the Kelvin-Helmholtz-stable nature of the large initial density contrast between the wind and CSM, supplemented by the slowly declining wind density profile (relative to homologously expanding ejecta). To support this interpretation, we present two-dimensional hydrodynamical simulations and derive the filament properties, including their speed, density, and temperature, all of which are consistent with observations. We suggest the filaments elongate until the wind and CSM densities become comparable at the contact discontinuity, which occurs within 1--10 years, and then truncate because the RTI halts. The subsequent KHI growth timescale across the current width of the filaments is longer t

Swarm intelligence optimization algorithms have gained significant attention due to their ability to solve complex optimization problems. However, the efficiency of optimization in large-scale problems limits the use of related methods. This paper presents a GPU-accelerated version of the Multi-Guiding Spark Fireworks Algorithm (MGFWA), which significantly improves the computational efficiency compared to its traditional CPU-based counterpart. We benchmark the GPU-MGFWA on several neural network black-box optimization problems and demonstrate its superior performance in terms of both speed and solution quality. By leveraging the parallel processing power of modern GPUs, the proposed GPU-MGFWA results in faster convergence and reduced computation time for large-scale optimization tasks. The proposed implementation offers a promising approach to accelerate swarm intelligence algorithms, making them more suitable for real-time applications and large-scale industrial problems. Source code is released at https://github.com/mxxxr/MGFWA.

In this paper, we propose view-dependent projection (VDP) to facilitate point cloud segmentation, designing efficient 3D-to-2D mapping that dynamically adapts to the spatial geometry from view variations. Existing projection-based methods leverage view-independent projection in complex scenes, relying on straight lines to generate direct rays or upward curves to reduce occlusions. However, their view independence provides projection rays that are limited to pre-defined parameters by human settings, restricting point awareness and failing to capture sufficient projection diversity across different view planes. Although multiple projections per view plane are commonly used to enhance spatial variety, the projected redundancy leads to excessive computational overhead and inefficiency in image processing. To address these limitations, we design a framework of VDP to generate data-driven projections from 3D point distributions, producing highly informative single-image inputs by predicting rays inspired by the adaptive behavior of fireworks. In addition, we construct color regularization to optimize the framework, which emphasizes essential features within semantic pixels and suppresses

As optimization problems grow increasingly complex and diverse, advancements in optimization techniques and paradigm innovations hold significant importance. The challenges posed by optimization problems are primarily manifested in their non-convexity, high-dimensionality, black-box nature, and other unfavorable characteristics. Traditional zero-order or first-order methods, which are often characterized by low efficiency, inaccurate gradient information, and insufficient utilization of optimization information, are ill-equipped to address these challenges effectively. In recent years, the rapid development of large language models (LLM) has led to substantial improvements in their language understanding and code generation capabilities. Consequently, the design of optimization algorithms leveraging large language models has garnered increasing attention from researchers. In this study, we choose the fireworks algorithm(FWA) as the basic optimizer and propose a novel approach to assist the design of the FWA by incorporating multi-modal large language model(MLLM). To put it simply, we propose the concept of Critical Part(CP), which extends FWA to complex high-dimensional tasks, and

Pipedreams are combinatorial objects that compute Grothendieck polynomials. We introduce a new combinatorial object that naturally recast the pipedream formula. From this, we obtain the first direct combinatorial formula for the top degree components of Grothendieck polynomials, also known as the Castelnuovo-Mumford polynomials. We also prove the inverse fireworks case of a conjecture of Mészáros, Setiabrata, and St. Dizier on the support of Grothendieck polynomials.

Training deep neural networks (DNNs) directly on edge devices has attracted increasing attention, as it offers promising solutions to challenges such as domain adaptation and privacy preservation. However, conventional DNN training typically requires large-scale datasets, which imposes prohibitive overhead on edge devices-particularly for emerging large language model (LLM) tasks. To address this challenge, a DNN-free method (ie., dataset sampling without DNN), named NMS (Near-Memory Sampling), has been introduced. By first conducting dimensionality reduction of the dataset and then performing exemplar sampling in the reduced space, NMS avoids the architectural bias inherent in DNN-based methods and thus achieves better generalization. However, The state-of-the-art, NMS, suffers from two limitations: (1) The mismatch between the search method and the non-monotonic property of the perplexity error function leads to the emergence of outliers in the reduced representation; (2) Key parameter (ie., target perplexity) is selected empirically, introducing arbitrariness and leading to uneven sampling. These two issues lead to representative bias of examplars, resulting in degraded accuracy

This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the selection strategy lead to the contribution of the firework with the best fitness (core firework) for the optimization overwhelms the contributions of the rest of fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which can greatly enhance the exploitation ability of non-core fireworks by using independent selection operator and increase the exploration capacity by crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions suggest that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, the standard particle swarm optimization (SPSO) in 2007 and the most recent SPSO in 2011 in term of convergence performance.

We construct an explicit model for the black hole to white hole transition (known as the black hole fireworks scenario) using the cut-and-paste technique. We model a black hole collapse using the evolution of a time-like shell in the background of the loop quantum gravity inspired metric. We then use the space-like shell analysis to construct the firework geometry. Our simple and well defined analysis removes some subtle issues that were present in the previous literature. In particular, we demonstrate that the null energy condition must be violated for the bounce. We also calculate the proper time scales required for the black to white hole transition, which in any valid scenario must be shorter than the evaporation time scale. In contrast, we show that the bouncing time for the distant observer can be chosen arbitrarily, since it is determined by how one cuts and pastes the spacetimes outside the event horizon, and thus does not have any obvious connection to quantum gravity effects.

Highly magnetized neutron stars are a source of extreme transients observed in different bands, like the fast radio burst (FRB) and associated hard X-ray burst from the Galactic magnetar SGR 1935+2154. The origin of such outbursts, hard X-rays on the one hand and millisecond duration FRBs on the other hand, is still unknown. We present a global model for various kinds of such magnetar outbursting activities. Crustal surface motions are expected to twist the inner magnetar magnetosphere by shifting the frozen-in footpoints of magnetic field lines. We discuss criteria for the development of instabilities of 3D twisted flux bundles in the force-free dipolar magnetospheres and compare their energetic properties to observations of magnetar X-ray flares. We then review a recently developed FRB generation mechanism in the outer magnetosphere of a magnetar. The strong magnetic pulse induced by a magnetar flare collides with the current sheet of the magnetar wind, compresses and fragments it into a self-similar chain of magnetic islands. Time-dependent plasma currents created during their collisions produce relatively narrow-band GHz emission with luminosities sufficient to explain bright e

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the ori

Fireworks algorithm is a new type of intelligent optimization algorithm. Because of its fast convergence speed, easy implementation, explosiveness, diversity, simplicity and randomness, it has attracted more and more attention in many research fields recently. This paper introduces the background, composition, improvement idea of fireworks algorithm (analysis and improvement of operator, improvement of hybrid algorithm), and its application in continuous optimization, discrete optimization, single-objective optimization, multi-objective optimization and other fields. Finally, the future research directions of fireworks algorithm are summarized, including theoretical analysis, operator analysis and improvement, hybrid algorithm research and algorithm application.